{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import theano\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import MDBN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding a layer with 559 input and 40 outputs\n",
      "Adding a layer with 19940 input and 400 outputs\n",
      "Adding a layer with 400 input and 40 outputs\n",
      "Adding a layer with 80 input and 24 outputs\n",
      "Adding a layer with 24 input and 3 outputs\n"
     ]
    }
   ],
   "source": [
    "import AML\n",
    "me_DBN, ge_DBN, dm_DBN, top_DBN = AML.load_network('Exp_2016-12-11_1841_run_0.npz','../MDBN_run')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datafiles = AML.prepare_AML_TCGA_datafiles('../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ME_output, _ = me_DBN.MLP_output_from_datafile(datafiles['ME'], datadir='../data')\n",
    "GE_output, _ = ge_DBN.MLP_output_from_datafile(datafiles['GE'], datadir='../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 79.5, 172.5, -0.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zNbEkWyI3J48nq7BSXkRW9ZPHRY5/Y88OpkDWwCq59pFhZxzwMCFKf/dIxlbd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb52890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint_layer = np.concatenate([ME_output, GE_output],axis=1)\n",
    "plt.imshow(joint_layer, cmap='gray',interpolation='none')\n",
    "plt.axis('tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x12de4e310>,\n",
       "  <matplotlib.axis.XTick at 0x12de6bc10>,\n",
       "  <matplotlib.axis.XTick at 0x12baaae50>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g7i6MBFcpNYeDtJnZEHveJXiDcc60XK6TroqvCZsi70g6XQH8s7wwn/SKLm0+B3wA+DXw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12de28190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_output = top_DBN.get_output(theano.shared(joint_layer,borrow=True))\n",
    "plt.imshow((top_output>0.8)*np.ones_like(top_output)-(top_output<0.2)*np.ones_like(top_output),interpolation='none',extent=[0,3,385,0])\n",
    "plt.colorbar()\n",
    "plt.axis('tight')\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x12bb77950>,\n",
       "  <matplotlib.axis.XTick at 0x12de9c090>,\n",
       "  <matplotlib.axis.XTick at 0x12bb37cd0>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rcyd2tyUdBBwHHERWg+k6Sfu77L2ZpdfV3RrTrbZcSKHhiIh4JH+6LdlYyKbi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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fac30d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(top_output, interpolation='none',extent=[0,3,385,0])\n",
    "plt.axis('tight')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 127.,    3.,    0.,    2.,    1.,    0.,    1.,    2.,    3.,   34.]),\n",
       "  array([  53.,    2.,    0.,    3.,    3.,    3.,    1.,    5.,    2.,  101.]),\n",
       "  array([ 69.,   1.,   1.,   0.,   0.,   0.,   0.,   4.,   0.,  98.])],\n",
       " array([  1.78998524e-10,   1.00000000e-01,   2.00000000e-01,\n",
       "          3.00000000e-01,   4.00000000e-01,   5.00000000e-01,\n",
       "          6.00000000e-01,   7.00000000e-01,   8.00000000e-01,\n",
       "          9.00000000e-01,   1.00000000e+00]),\n",
       " <a list of 3 Lists of Patches objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aIcmSfS7YvJmnDx8eXmEy8CWN30tAdemTmZlxlNKUkU3pJLkiyZNJ/neSfzaq/QzTxMT2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\naoSBL0mNMPAlqREGviQ14v8B5f03gJcjz+sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12aff4810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 127.,    3.,    0.,    2.,    1.,    0.,    1.,    2.,    3.,   34.]),\n",
       " array([  1.78998524e-10,   9.99999406e-02,   1.99999881e-01,\n",
       "          2.99999821e-01,   3.99999762e-01,   4.99999702e-01,\n",
       "          5.99999642e-01,   6.99999583e-01,   7.99999523e-01,\n",
       "          8.99999464e-01,   9.99999404e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gr4sVwL3AMb3tl8y67hn2xZXApr39AHwPWDbr2ifQF28ETgfu3s/+oXJz0iP8QR7AWgd8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12be48750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "code = (top_output[:,0:2] > 0.5) * np.ones_like(top_output[:,0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import find_unique_classes\n",
    "U = find_unique_classes(code)\n",
    "cl = U[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  1.,  3.,  1.,  0.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  3.,  3.,  1.,  0.,  0.,  1.,  1.,  1.,  2.,  0.,  1.,\n",
       "        1.,  0.,  1.,  1.,  3.,  1.,  0.,  0.,  1.,  3.,  1.,  1.,  1.,\n",
       "        2.,  1.,  3.,  1.,  1.,  1.,  2.,  0.,  3.,  1.,  1.,  1.,  0.,\n",
       "        0.,  1.,  3.,  0.,  1.,  3.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,\n",
       "        0.,  0.,  1.,  1.,  3.,  1.,  3.,  1.,  2.,  1.,  1.,  3.,  1.,\n",
       "        1.,  1.,  0.,  3.,  3.,  1.,  3.,  1.,  3.,  2.,  2.,  0.,  1.,\n",
       "        1.,  1.,  1.,  2.,  1.,  1.,  0.,  2.,  0.,  1.,  1.,  0.,  2.,\n",
       "        2.,  3.,  0.,  1.,  2.,  1.,  1.,  2.,  3.,  0.,  3.,  2.,  0.,\n",
       "        2.,  1.,  3.,  1.,  1.,  1.,  1.,  1.,  0.,  1.,  3.,  0.,  1.,\n",
       "        1.,  1.,  1.,  3.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  1.,  0.,\n",
       "        0.,  0.,  0.,  1.,  0.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,  0.,\n",
       "        1.,  1.,  0.,  3.,  3.,  0.,  0.,  0.,  0.,  2.,  0.,  1.,  0.,\n",
       "        0.,  3.,  0.,  0.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 46.,  87.,  15.,  25.]),\n",
       " array([ 0.  ,  0.75,  1.5 ,  2.25,  3.  ]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CZmy81gVo2cZrXcCMjde6gHVpJT31S4C7q+pwVf0Q+Ftg5zBlbSTjtS5gxsZrXYCWbbzW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\nGOqS1BBDXZIaYqhLUkMMdUlqyP8D6jyLm6LQB08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c1b42d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cl,bins=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check Survival curves for the different classes\n",
    "==============================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "id=[]\n",
    "with open('../data/'+datafiles['ME']) as f:\n",
    "    my_csv = csv.reader(f,delimiter='\\t')\n",
    "    id = my_csv.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat={}\n",
    "with open('../data/AML/AML_clinical_data2.csv') as f:\n",
    "    reader = csv.reader(f, delimiter=',')\n",
    "    for row in reader:\n",
    "        patient_id=row[0]\n",
    "        stat[patient_id]=(row[4],row[7],row[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following case IDs were  not found in clinical data\n",
      "No data for TCGA-AB-2887\n",
      "No data for TCGA-AB-2891\n",
      "No data for TCGA-AB-2918\n",
      "No data for TCGA-AB-2921\n",
      "No data for TCGA-AB-2930\n",
      "No data for TCGA-AB-2940\n",
      "No data for TCGA-AB-2943\n",
      "No data for TCGA-AB-2944\n",
      "No data for TCGA-AB-2946\n",
      "No data for TCGA-AB-2975\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "time_list = []\n",
    "event_list = []\n",
    "group_list = []\n",
    "print('The following case IDs were  not found in clinical data')\n",
    "for index, key in enumerate(id[1:]):\n",
    "    m = re.match('TCGA-\\w+-\\d+', key)\n",
    "    patient_id = m.group(0)\n",
    "    if patient_id in stat:\n",
    "        patient_stat = stat[patient_id]\n",
    "        add_group = True\n",
    "        try:\n",
    "            time_list.append(float(patient_stat[2]))\n",
    "            event_list.append(1)\n",
    "        except ValueError:\n",
    "            try:\n",
    "                time_list.append(float(patient_stat[1]))\n",
    "                event_list.append(0)\n",
    "            except ValueError:\n",
    "                print('No data for %s' % patient_id)\n",
    "                add_group = False\n",
    "        if add_group:\n",
    "            group_list.append(cl[index])\n",
    "    else:\n",
    "        print(patient_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d90de50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A7Fq/0R1gW1rI9bB5hVzwnh0cHwo8E7RzAXBo6HuuJffmexlwdq3bUKJ984D1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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d8fcc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(time_list,event_observed=event_list)\n",
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SS/nZz36WMD3bV7/6VR599NGk6dkuv/xyNm7cyB/+8AcqPGs3ZYov78Srqzv59298g3//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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d961750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 0)\n",
    "kmf.fit(T[ix], E[ix], label='group 0')\n",
    "ax=kmf.plot()\n",
    "for i in range(1,4):\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
